Systems and methods for prediction of tumor treatment response to using texture derivatives computed from quantitative ultrasound parameters

ABSTRACT

Systems and methods for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that can be used to assess a prediction of tumor response to different chemotherapy treatment regimens are provided. For instance, the imaging biomarkers can be used to subtype tumors that have resistance to certain chemotherapy regimens prior to drug exposure. These imaging biomarkers can therefore be useful for predicting tumor response and for assessing the prognostic value of particular treatment regimens.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent application Ser. No. 15/739,003, which is a 371 of PCT/CA2016/050727, filed on Jun. 21, 2016, and entitled “SYSTEMS AND METHODS FOR PREDICTION OF TUMOR RESPONSE TO CHEMOTHERAPY USING PRE-TREATMENT QUANTITATIVE ULTRASOUND PARAMETERS,” which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/182,773, filed on Jun. 22, 2015, and entitled “SYSTEMS AND METHODS FOR PREDICTION OF TUMOR RESPONSE TO CHEMOTHERAPY USING PRE-TREATMENT QUANTITATIVE ULTRASOUND PARAMETERS,” all of which are herein incorporated by reference in their entirety.

BACKGROUND OF THE INVENTION

The field of the invention is systems and methods for quantitative ultrasound (“QUS”). More particularly, the invention relates to systems and methods for using quantitative ultrasound to generate imaging biomarkers from which tumors can be classified based on a prediction of the tumor's response to different treatment regimens.

QUS techniques examine the frequency-dependent backscatter of tissues independent of the instrument settings. Based on data acquired using these techniques, quantitative parameters including mid-band fit (“MBF”), spectral slope (“SS”), spectral 0-Mhz intercept (“SI”), spacing among scatterers (“SAS”), attenuation coefficient estimate (“ACE”), average scatterer diameter (“ASD”), and average acoustic concentration (“AAC”) can be computed.

There is a current desire for the discovery of imaging biomarkers that allow early prediction of tumor response to therapy.

SUMMARY OF THE INVENTION

The present invention overcomes the aforementioned drawbacks by providing a computer-implemented method for assessing a prediction of tumor response to a treatment using an ultrasound system. The method includes acquiring ultrasound echo signal data from a subject using the ultrasound system, where the ultrasound echo signal data are acquired from a volume containing a tumor. Parametric map data are generated from the ultrasound echo signal data, where the parametric map data have pixel values associated with one or more different quantitative parameters computed from the acquired ultrasound echo signal data. Texture derivative map data are generated by inputting the parametric map data to two or more passes of a texture analysis algorithm, generating output as the texture derivative map data, where the texture derivative map data have pixel values associated with one or more different texture derivatives. The tumor is then classified by applying a classifier to the texture derivative map data, where classifying the tumor indicates a prediction of the tumor's response to a treatment.

It is another aspect of the invention to provide a computer-implemented method for using an ultrasound system to assess a prognosis for a subject who will be treated with a particular tumor treatment regimen. The method includes acquiring ultrasound echo signal data from a subject using the ultrasound system, where the ultrasound echo signal data are acquired from a volume containing a tumor. Parametric map data are generated from the ultrasound echo signal data, where the parametric map data have pixel values associated with one or more different quantitative parameters computed from the acquired ultrasound echo signal data. Texture derivative map data are generated by inputting the parametric map data to two or more passes of a texture analysis algorithm, generating output as the texture derivative map data, where the texture derivative map data have pixel values associated with one or more different texture derivatives. The tumor is then classified based on a prognosis for the subject following treatment with a particular tumor treatment regimen. The tumor is classified in this manner by applying a classifier to the texture derivative map data, where classifying the tumor indicates a prognosis for the subject following treatment with a particular tumor treatment regimen. As an example, the prognosis for the subject can be based on an estimator, which may include a progression-free survival, a survival rate, and a survival time.

The foregoing and other aspects and advantages of the invention will appear from the following description. In the description, reference is made to the accompanying drawings that form a part hereof, and in which there is shown by way of illustration a preferred embodiment of the invention. Such embodiment does not necessarily represent the full scope of the invention, however, and reference is made therefore to the claims and herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

FIG. 1 is a flowchart setting forth the steps of an example method for using quantitative ultrasound to generate imaging biomarkers that can be used to classify a tumor based on a prediction of treatment response.

FIG. 2 is an example of a computer system that can implement the methods and algorithms described here.

FIGS. 3A-3D depict an example process of segmenting an image of a tumor into a region-of-interest (“ROI”) corresponding to the tumor core and an ROI corresponding to a margin around the tumor core.

FIGS. 4A-4B depict a representative B-mode image (FIG. 4A) and corresponding tumor core and tumor margin ROIs (FIG. 4B).

FIGS. 5A-5H illustrate comparisons of a responding and a non-responding patient's tumor. Shown are original B-mode images (FIGS. 5A and 5E); SI parametric images (FIGS. 5B and 5F); ASD parametric images (FIGS. 5C and 5G) with core and margin ROIs outlined in white; and H&E-stained post-surgical breast specimen (FIGS. 5D and 5H) with pink indicating normal breast tissue, light pink indicating fibrosis, and purple indicating residual tumor tissue.

FIGS. 6A and 6B illustrate Kaplan-Meier recurrence-free survival curves based on post-surgical histopathology (FIG. 6A) and the QUS-based analysis methods described here (FIG. 6B).

FIG. 7 illustrates an example workflow for generating both texture feature maps and texture derivative maps from quantitative ultrasound parameter maps.

FIG. 8 is a flowchart setting forth the steps of an example method for classifying a tumor based at least on texture derivative maps (e.g., texture-of-texture maps) generated from quantitative ultrasound parameter maps.

FIG. 9 shows example parameter map data and texture map data.

DETAILED DESCRIPTION OF THE INVENTION

Described here are systems and methods for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that can be used to assess a prediction of tumor response to different chemotherapy treatment regimens. For instance, the imaging biomarkers can be used to subtype tumors that have resistance to certain chemotherapy regimens prior to drug exposure. These imaging biomarkers can therefore be useful for predicting tumor response.

Thus, described here are systems and methods for using a quantitative ultrasound technique to classify tumors, such as locally advanced breast tumors and others, in terms of their chemo-responsiveness prior to beginning neoadjuvant chemotherapy treatment. This technique permits the identification of tumor subtypes that have resistance to certain chemotherapy drugs without the need for exposing the patient to those drugs.

Referring now to FIG. 1, a flowchart is illustrated as setting forth the steps of an example method for using quantitative ultrasound (“QUS”) techniques to generate imaging biomarkers that indicate an efficacy of chemotherapy treatment regimens on a tumor. Raw echo signal data (e.g., ultrasound radio frequency (“RF”) data) are acquired from the subject, as indicated at step 102. The raw echo signal data are acquired in response to ultrasound transmitted to the subject. The transmitted ultrasound is preferably conventional-frequency, but can also be high-frequency ultrasound. In some instances, it may also be beneficial to use combinations of low-frequency and high-frequency ultrasound depending on the depth of the tissue and the desired imaging resolution. For instance, higher ultrasound frequencies are capable of increasing imaging resolution, but at the cost of limiting the penetration depth of the ultrasound. For example, ultrasound frequencies in the range of 20-60 MHz can achieve imaging resolutions in the range of 30-80 μm, whereas ultrasound frequencies in the range of 1-20 MHz can achieve imaging resolutions of 30 μm to about 1.5 mm.

By way of example, the raw echo signal data can be acquired from a subject using an ultrasound system operating at a conventional ultrasound frequency, such as 6 MHz or 10 MHz. Alternatively, the ultrasound system can be operated to generate high frequency ultrasound, such as greater than 20 MHz. The echo signals in the ultrasound RF data can be obtained in a number of differently oriented image planes.

Images of the subject that depict the subject's anatomy are also provided, as indicated at step 104. As one example, the images can be B-mode images that are provided by acquiring the images from the subject, or by retrieving previously acquired B-mode images from data storage.

One or more regions-of-interest (“ROIs”) are then identified in the provided images of the subject, as indicated at step 106. In general, one or more ROIs are identified for each tumor depicted in the provided images. Preferably, at least two ROIs are identified for each tumor: one ROI being associated with the core of the tumor and one ROI being associated with the tumor margin.

The ROIs are then divided into RF blocks, as indicated at step 108. As one example, each block includes twenty scan lines that are each ten wavelengths long. This example corresponds to blocks that are 2 mm×2 mm assuming a speed of sound of 1540 m/s. As an example, each selected ROI can be divided into RF blocks using a sliding window approach with or without overlap between adjacent windows. Each window is advantageously sized to be larger than the minimum size required to obtain reliable spectral parameters that are independent of window length. For instance, the window can be sized to be at least as large as ten wavelengths of the transmitted ultrasound.

For each RF block, a normalized power spectrum is computed from the acquired raw echo signal data to make the analysis of the raw echo signal data system-independent, as indicated at step 110. By way of example, the raw echo signal data can be normalized on a sliding window basis using reference data obtained from a tissue-mimicking phantom, a planar reflector, or the like. To this end, reference echo signal data can optionally be provided, as indicated at step 112.

For example, a tissue-mimicking phantom composed of agar gel embedded with glass microspheres can be used to obtain reference echo signal data. Preferably, such reference echo signal data can be used for normalizing the mean power spectrum on which linear regression analyses will be performed in order to extract a number of quantitative ultrasound parameters.

As another example, a planar reflector, such as a Plexiglas planar reflector, can be used to obtain reference echo signal data to be used when computing SAS in order to avoid affecting the estimation of SAS in the tissue by the glass scatterers in the tissue-mimicking phantom. Reference echo signal data obtained from such a planar reflector are preferably obtained at a plurality of different depths to cover the potential tissue depths in the region of the subject. As an example, twelve equally spaced depths ranging from 1-6 cm can be utilized.

For a given data window, the corresponding reference window can be selected by nearest neighbor interpolation. Spectral normalization of the mean power spectrum may be performed using RF echoes obtained from a reference phantom, e_(p)(t, x_(i)), to remove the system transfer function. The mean normalized power spectrum, S(f), of a window can be written as,

$\begin{matrix} {{{S(f)} = \frac{\sum\limits_{i = N}^{M}{{{FFT}\left( {e_{s}\left( {t,x_{i}} \right)} \right)}}^{2}}{\sum\limits_{N}^{M}{{{FFT}\left( {e_{p}\left( {t,x_{i}} \right)} \right)}}^{2}}};} & (1) \end{matrix}$

where f is frequency, t is the time-gated RF echo segment in the window, x_(i) is the i^(th) lateral position in the window, and FFT( . . . ) is the Fast Fourier Transform operator.

From the normalized power spectra, one or more parametric maps are generated for each ROI, as indicated at step 114. For instance, the parametric maps are images whose pixel values are representative of quantitative ultrasound parameters computed from the raw echo signal data. Examples of such parameters include mid-band fit (“MBF”), spectral slope (“SS”), spectral 0-Mhz intercept (“SI”), spacing among scatterers (“SAS”), attenuation coefficient estimate (“ACE”), average scatterer diameter (“ASD”), and average acoustic concentration (“AAC”). Quantitative ultrasound parameters such as ASD and AAC can be estimated by fitting a theoretical tissue backscatter model to the measured backscatter signal from the tissue of interest. For example, estimated and theoretical backscatter coefficients can be used to compute ESD and EAC.

As one example, a linear regression analysis, such as a least squares fit, can be applied to a normalized power spectrum to extract the MBF, SS, and SI parameters as follows:

S(f)=SS·f+SI  (2);

MBF=SS·f_(c)+SI  (3);

where f_(c) is the frequency at the center of the analysis bandwidth, which may be the −6 dB frequency bandwidth. More generally, the bandwidth can be determined empirically, such as from the power spectrum of a reference phantom or planar reflector.

One or more features, such as first-order statistical measures, second-order statistical measures, and image quality measures, are next extracted from the parametric maps, as indicated at step 116. Then, as indicated at step will 118 and as will be described below in more detail, the one or more features are provided to a classifier to subtype the tumor and to provide a statistical estimate indicating whether the tumor is likely to have resistance to certain chemotherapy regimens prior to exposing the tumor to the chemotherapy drugs.

First-order statistics are generally computed from a function that measures the probability of a certain pixel occurring in an image and, therefore, they depend on individual pixel values and not on the interaction of neighboring pixel values. By way of example, the first-order statistical measure may be the mean of intensities in a parametric map. Alternatively, the first-order statistical measure may be the standard deviation, skewness, or kurtosis of a parametric map.

In general, second-order statistics are computed from a probability function that measures the probability of a pair of pixel values occurring at some offset in an image. This probability function is typically referred to as a “co-occurrence matrix” because it measures the probability of two pixel values co-occurring at the given offset. An example of the co-occurrence matrix is the gray level co-occurrence matrix (“GLCM”). These second-order statistics can generally be referred to as textural features of an image. The application of textural analysis on the quantitative ultrasound parametric maps, where instrument dependencies are preferably removed via the aforementioned normalization, provides advantageous information for the classification techniques described later. By way of example, second-order statistical measures may include contrast, energy, homogeneity, or correlation. Alternatively, the second-order statistical measure could include other second-order statistics, including autocorrelation, dissimilarity, GLCM variance, entropy, cluster shade, cluster prominence, and maximum probability.

The GLCM represents, statistically, the angular relationship between neighboring pixels as well as the distance between them. Based on the statistical information provided by a GLCM, several textural features can be defined and extracted.

Contrast (“CON”) represents a measure of difference between the lowest and highest intensities in a set of pixels. Energy (“ENE”) measures the frequency of occurrence of pixel pairs and quantifies its power as the square of the frequency of gray-level transitions. Homogeneity (“HOM”) measures the incidence of pixel pairs of different intensities; thus, as the frequency of pixel pairs with close intensities increases, HOM increases. Correlation (“COR”) measures the intensity correlation between pixel pairs.

In some embodiments, to estimate second-order statistical measures the computed parametric maps are processed using a GLCM-based texture analysis process to extract the aforementioned second-order statistical measures, which may also be referred to as textural features. A GLCM is an N_(g)×N_(g) matrix, where N_(g) is the number of quantized gray levels in the image for which the GLCM is computed (e.g., the parametric maps in this instance). Each element in the GLCM, p(i,j), is a statistical probability value for changes between the i^(th) and j^(th) gray levels at a particular displacement distance, d, and angle, θ. Thus, given p(i,j) as an element in an N_(g)×N_(g) GLCM, the above-mentioned textural parameters can be defined as follows:

$\begin{matrix} {{{CON} = {{\sum\limits_{k = 0}^{N_{g} - 1}{{k^{2}\left( {\sum\limits_{j = 1}^{N_{g}}{\sum\limits_{j = 1}^{N_{g}}{p\left( {i,j} \right)}}} \right)}\mspace{14mu} {with}\mspace{14mu} k}} = {{i - j}}}};} & (4) \\ {{{ENE} = {\sum\limits_{i = 1}^{N_{g}}{\sum\limits_{j = 1}^{N_{g}}{p\left( {i,j} \right)}^{2}}}};} & (5) \\ {{{HOM} = {\sum\limits_{i = 1}^{N_{g}}{\sum\limits_{j = 1}^{N_{g}}\frac{p\left( {i,j} \right)}{1 + {{i - j}}}}}};} & (6) \\ {{{COR} = \frac{\sum\limits_{i = 1}^{N_{g}}{\sum\limits_{j = 1}^{N_{g}}{\left( {i - \mu_{x}} \right)\left( {j - \mu_{y}} \right){p\left( {i,j} \right)}}}}{\sigma_{x}\sigma_{v}}};} & (7) \end{matrix}$

where μ_(x) and μ_(y) are the means for the columns and rows, respectively, of the GLCM,

$\begin{matrix} {{\mu_{x} = {\overset{N_{g}}{\sum\limits_{i = 1}}{\overset{N_{g}}{\sum\limits_{j = 1}}{i \cdot {p\left( {i,j} \right)}}}}};} & (8) \\ {{\mu_{y} = {\sum\limits_{i = 1}^{N_{g}}{\sum\limits_{j = 1}^{N_{g}}{j \cdot {p\left( {i,j} \right)}}}}};} & (9) \end{matrix}$

and where σ_(x) and σ_(y) are the standard deviations for the columns and rows, respectively, of the GLCM,

$\begin{matrix} {{\sigma_{x}^{2} = {\sum\limits_{i = 1}^{N_{g}}{\sum\limits_{j = 1}^{N_{g}}{\left( {i - \mu_{x}} \right)^{2}N{p\left( {i,j} \right)}}}}};} & (10) \\ {\sigma_{y}^{2} = {\sum\limits_{i = 1}^{N_{g}}{\sum\limits_{j = 1}^{N_{g}}{\left( {j - \mu_{y}} \right)^{2} \cdot {{p\left( {i,j} \right)}.}}}}} & (11) \end{matrix}$

A number of different GLCMs can be constructed for each parametric map. For example, sixteen symmetric GLCMs can be constructed considering each pixel's neighbors located at the displacement distances, d, of one to four pixels with angular values, θ, of 0-135 degrees with 45 degree increments. The second-order statistical measures, or textural features, can then be extracted from the corresponding GLCMs of each QUS parametric map and consequently averaged to produce the computed second-order statistical measures.

Image quality measures can include signal-to-noise ratio (“SNR”) and contrast-to-noise ratio (“CNR”). As another example, image quality features can be defined to compare pixel intensities between two ROIs, such as an ROI associated with a tumor core and an ROI associated with a tumor margin. Two such image quality features are a core-to-margin ratio (“CMR”) and a core-to-margin contrast ratio (“CMCR”), which can be computed as follows:

$\begin{matrix} {{{CMR} = \frac{{mean}\left( {{RO}I_{core}} \right)}{\sigma \left( {ROI_{margin}} \right)}};} & (12) \\ {{{CMCR} = \frac{{{{mean}\left( {ROI}_{core} \right)} - {{mean}\left( {{RO}I_{margin}} \right)}}}{\frac{1}{2}\left( {{\sigma \left( {ROI_{core}} \right)} + {\sigma \left( {ROI_{margin}} \right)}} \right)}};} & (13) \end{matrix}$

where mean(ROI_(core)) is the mean image intensity value for a parametric map in the ROI associated with the tumor core, mean(ROI_(margin)) is the mean image intensity value for a parametric map in the ROI associated with the tumor margin, σ(ROI_(core)) is the standard deviation of image intensity values for a parametric map in the ROI associated with the tumor core, and σ(ROI_(margin)) is the standard deviation of image intensity values for a parametric map in the ROI associated with the tumor margin.

Referring again to FIG. 1, the step of providing the one or more features extracted from the parametric maps to a classifier to subtype the tumor can include using a discriminant analysis, such as a linear discriminant analysis (“LDA”). As one example, a Fisher's linear discriminant (“FLD”) classifier can be used. In other embodiments, other classifiers, such as a support vector machine (“SVM”) classifier or a k-nearest neighbors (“k-NN”) classifier, can be used. In some embodiments, the tumor can be classified based on a prediction of the tumor's response to a chemotherapy treatment. In some other embodiments, the tumor can be classified based on a prognosis for the subject following treatment with a particular tumor treatment regimen.

An FLD-based classifier is a linear classifier that projects multidimensional data onto a feature space that maximizes the ratio of between-class to within-class variance, and performs well when the data can be separated by a line. To classify a tumor to indicate its response to different treatment regimens using LDA (e.g., an FLD-based classifier), the linear discriminant can be trained and tested using, for example, a leave-one-out approach for the tissue being analyzed. Similar training and testing can be performed using other classifiers, such as SVM and k-NN. Examples of tissues that can be analyzed include, but are not limited to, tissues in the breast, liver, brain, prostate, kidney, bladder, gallbladder, spleen, cervix, blood vessels, muscle, and bone. This training can be performed in real-time or, preferably, can be performed off-line with the results stored in a feature set database that can be provided during processing. Such a feature set database includes combinations of the first-order statistical measures, second-order statistical measures, and image quality measures that maximize, or otherwise provide desired levels of, the specificity and sensitivity of the tumor classification. As discussed above, these feature sets can define imaging biomarkers for the tumor and can indicate the tumor response to different treatment regimens before exposing the subject to chemotherapy.

SVM-based classifiers build a model (e.g., from training data) to have the largest possible gap between the classes, and then predict the class association of test data samples based on which side of the gap they fall on. As one example, a Gaussian radial basis function can be used as the kernel function for an SVM-based classifier. In general, the kernel function defines how data samples will be mapped into the new feature space, called kernel space. SVM model parameters can be optimized using a grid search. In a leave-one-out validation scheme, a k-NN classifier can be used to predict the class association of a test point in the feature space based on the class that forms the majority of the points neighboring the point of interest, and based also on the distance between those points and the point of interest. The SVM and k-NN based classifiers are non-linear classifiers, which are advantageous when the classes cannot be separated by a line and when a large number of features is available.

Referring now to FIG. 2, a block diagram of an example computer system 200 that can be configured to classify tumors based on predicted treatment response using the quantitative ultrasound techniques described above, is illustrated. The echo signal data can be provided to the computer system 200 from an ultrasound system, or from a data storage device, and is received in a processing unit 202.

In some embodiments, the processing unit 202 can include one or more processors. As an example, the processing unit 202 may include one or more of a digital signal processor (“DSP”) 204, a microprocessor unit (“MPU”) 206, and a graphics processing unit (“GPU”) 208. The processing unit 202 can also include a data acquisition unit 210 that is configured to electronically receive data to be processed, which may include echo signal data or digital images. The DSP 204, MPU 206, GPU 208, and data acquisition unit 210 are all coupled to a communication bus 212. As an example, the communication bus 212 can be a group of wires, or a hardwire used for switching data between the peripherals or between any component in the processing unit 202.

The DSP 204 can be configured to receive and processes the echo signal data. For instance, the DSP 204 can be configured to receive the echo signal data and form a digital image therefrom. The MPU 206 and GPU 208 can be configured to process the echo signal data, or a digital image formed therefrom, in conjunction with the DSP 204. As an example, the MPU 206 can be configured to control the operation of components in the processing unit 202 and can include instructions to perform processing of the echo signal data, or a digital image formed therefrom, on the DSP 204. Also as an example, the GPU 208 can process image graphics. Also

In some embodiments, the DSP 204 can be configured to process the echo signal data, or a digital image formed therefrom, received by the processing unit 202 in accordance with the algorithms described herein. Thus, the DSP 204 can be configured to generate parametric maps; to compute first-order order statistical measures, second-order statistical measures, and image quality measures of the parametric maps; and to classify tumors based on the first-order order statistical measures, second-order statistical measures, and image quality measures of the parametric maps.

The processing unit 202 preferably includes a communication port 214 in electronic communication with other devices, which may include a storage device 216, a display 218, and one or more input devices 220. Examples of an input device 220 include, but are not limited to, a keyboard, a mouse, and a touch screen through which a user can provide an input.

The storage device 216 is configured to store echo signal data, digital images, or both, whether provided to or processed by the processing unit 202. The display 218 is used to display images, such as images that may be stored in the storage device 216, and other information. Thus, in some embodiments, the storage device 216 and the display 218 can be used for displaying the parametric maps, and for outputting other information such as data plots or other reports based on statistical measures computed from the parametric maps, including information indicating a classification of tumors and predicted treatment response for tumors.

The processing unit 202 can also be in electronic communication with a network 222 to transmit and receive data, including echo data, images, and other information. The communication port 214 can also be coupled to the processing unit 202 through a switched central resource, for example the communication bus 212.

The processing unit 202 can also include a temporary storage 224 and a display controller 226. As an example, the temporary storage 224 can store temporary information. For instance, the temporary storage 224 can be a random access memory.

Example: Classifying Tumors in Locally Advanced Breast Cancer

In this example, tumors were classified based on predictions of tumor response to chemotherapy treatment regimens using the quantitative ultrasound analysis methods described above. The results indicate that the methods are capable of classifying tumor subtypes based on predicted treatment response, thereby providing diagnostic value for evaluating tumors and planning treatment strategies before treating subjects.

Materials and Methods

In this study, ultrasound RF data were collected from the affected breast of 56 patients with locally advanced breast cancer (“LABC”) prior to neoadjuvant chemotherapy. Patients in the study were recently diagnosed with locally advanced invasive breast cancer within one week of imaging. Breast cancers included invasive ductal carcinoma, invasive lobular carcinoma, and other forms of invasive cancer, including all grades. This included patients with tumors larger than 5 cm and/or tumors with locoregional lymph node, skin, and chest wall involvement.

Treatment regimens varied from 5-fluorouracil, epirubicin and cyclophosphamide followed by docetaxol (FEC-D), to Adriamycin followed by paclitaxel (AC-T), to taxol followed by herceptin varying from weekly to tri-weekly cycles. A summary of patient characteristics is provided in Table 1, which provides a summary of patient characteristics, with IDC=invasive ductal carcinoma, ILC=Invasive lobular carcinoma, and BTS=bulk tumor shrinkage (percent change in tumor size).

TABLE 1 Age (y) 49 ± 10 Pre-tx tumor size 6.3 ± 3.2 (cm) No. % Tumor subtype IDC 52 93 ILC 3 5 Other 1 2 Responders 42 75 BTS (%) 68 ± 47 Non-responders 14 25 BTS (%) −16 ± 57  

Breast ultrasound data were collected by an experienced breast sonographer with 10 years of experience using a clinical scanner (Sonix RP, Ultrasonix, Vancouver, Canada) employing a 6 MHz linear array transducer (L14-5-60), sampling at a rate of 40 MHz, with the focus set at the midline of the tumor and maximum depth set to 4-6 cm, depending on tumor size and location. Standard B-mode imaging was used for anatomical navigation and acquisition location was determined based on the tumor location reported in biopsy findings. Approximately 3-5 image planes were acquired from the tumor, each 1 cm spaced apart, depending on the tumor size.

The region of interest (“ROI”) selection for quantitative ultrasound analysis was performed in a semi-automated manner. First, a 5 mm margin of the tumor was outlined manually from the B-mode breast image, as illustrated in FIG. 3A. Then, an ROI 302 containing the tumor core and an ROI 304 associated with a margin around the core was selected for each patient. In this example, the margin included an approximately 1 cm-wide rim around the tumor core. Otsu's threshold segmentation was applied to the image in FIG. 3A to obtain a binary mask of the core and margin (FIG. 3B). The binary mask was then refined through image dilation in order to obtain a uniform margin with smooth edges (FIG. 3C). The refined binary mask was further refined through hole filling in order to fill holes inside the core (FIG. 3D). This is the final mask that defined the tumor core ROI and the tumor margin ROI for subsequent quantitative ultrasound analyses. As another example, FIG. 4A is a representative conventional ultrasound image (B-mode image) of a patient's breast tumor, and FIG. 4B depicts its corresponding tumor core ROI 402 and tumor margin ROI 404.

The obtained core and margin ROIs were divided into RF blocks for QUS analysis. Each RF block included 20 scan lines each 10 wavelengths long. This approximately corresponded to a 2 mm×2 mm block (assuming a speed of sound of 1540 m/s). Normalized power spectrum was computed for each RF block using a phantom reference, and a parametric image was computed over each tumor ROI for each QUS parameter. The QUS parameters investigated were MBF, SS, SI, ACE, ASD, and AAC. From each parametric map corresponding to core ROI and margin ROI, seven features were computed: mean of intensity, texture features (contrast, correlation, energy, and homogeneity), core-to-margin ratio (CMR), and core-to-margin contrast ratio (CMCR).

Results

Results of response classification using different classifiers are provided in Table 2, which reports the sensitivity, specificity, and accuracy obtained after leave-one-out cross-validation. In each case, a wrapper-based sequential forward feature selection was used to obtain the set of features that yielded the highest classification accuracy, as shown in Table 2.

TABLE 2 Classifier Sensitivity (%) Specificity (%) Accuracy (%) FLD 79 64 75 SVM 90 64 82 k-NN 90 79 88

Table 3 presents the classification performance results obtained for different margin thicknesses used to generate ROI_(margin), including 3, 5, and 10 mm thicknesses. Results suggested that 5 mm is the optimal margin thickness for characterizing a patient's tumor responsiveness. The results in Table 3 are based on the classifier that performed the best for each margin thickness, which was the k-NN in all three cases.

TABLE 3 ROI_(margin) Thickness Sensitivity (%) Specificity (%) Accuracy (%) 3 mm 88 41 74 5 mm 90 79 88 10 mm  85 41 72

The features used in the classification model, and their corresponding level of statistical significance from t-tests, are reported in Table 4. The results in Table 4 indicate the optimal feature set obtained through sequential forward feature selection using the k-NN classifier and a 5 mm thick tumor margin ROI, along with the statistical significance of each parameter. P-values were obtained using an unpaired t-test (one tail, α=0.05) or Mann-Whitney test between the response groups, depending on parameter distribution normality.

TABLE 4 Parameter P-value ACE 0.019 SI_(margin) ^(mean) 0.118 SI_(core) ^(HOM) 0.179 MBF_(core) ^(mean) 0.192 SI^(CMR) 0.192 ASD^(CMCR) 0.234 SS_(core) ^(COR) 0.244 SS^(CMCR) 0.303 SI^(CMCR) 0.366

Results suggest that the statistical and image quality features of spectral parameters may provide more discriminatory information about the response characteristics of a tumor than backscatter model parameters, such as ASD. A Mann-Whitney test on the posterior probabilities of the good response and poor response groups demonstrated highly statistically significant results (p<0.001), further demonstrating the effectiveness of the k-NN based multiparametric classifier in differentiating between the two response groups.

FIGS. 5A-5H displays a panel of images related to one representative good response patient (FIGS. 5A-5D) and one representative poor response patient (FIGS. 5E-5H), including original B-mode images (FIGS. 5A, 5E); parametric images of the best spectral parameter, which was SI (FIGS. 5B, 5F); parametric images of the best backscatter-model-based parameter, which was ASD (FIGS. 5C, 5G); and eosin and hematoxylin (H&E) sections of the post-surgical breast specimen (FIGS. 5D, 5H). The best parameters were determined based on their dominance in the optimal feature set presented in Table 4. As seen in FIGS. 5A and 5E, a tumor in a B-mode image of a LABC patient's breast can be identified as a hypo-intense mass surrounded by a relatively hyper-intense fibroglandular tissue. As seen in FIGS. 5B, 5C, 5F, and 5G, parametric maps of SI and ASD hold further information about the tumor, with each ROI (core and margin) containing a unique textural pattern. The H&E section for the representative good response patient (FIG. 5D) demonstrates only fibroglandular tissue (pink stain) and fibrosis (light pink stain) remaining in the tumor “bed,” whereas the H&E section for the poor response patient (FIG. 5H) shows two distinct masses (purple stain) of residual tumor remaining after months of treatment.

FIGS. 6A and 6B illustrate Kaplan-Meier recurrence-free survival curves based on post-surgical histopathology (FIG. 6A) and the QUS-based analysis methods described here (FIG. 6B). Solid curves 602 represents responders and dashed curves 604 represents non-responders.

The data suggests that the QUS-based analysis methods described above are able to predict the 5-year survival of LABC patients who underwent chemotherapy. The results suggest that ultrasonic features both inside the tumor and in the periphery of the tumor are prognostic factors. To this end, the methods described here can also be used to classify a tumor based on a prognosis for the patient before the patient receives a particular treatment regimen. This prognosis can be based on an estimator, such as progression-free survival time, survival rate, or survival time. The classification of the tumor in this manner can provide additional diagnostic information that can be assessed when making treatment decisions, such as selecting the treatment regimen that is most likely to result in a successful prognosis.

QUS features inside the tumor may reflect tumor features such as cellularity and vascularity, which may describe the aggressiveness of the tumor. Because the SNR/CNR and margin features contributed to the classification of response, it is strongly suggested that the tumor periphery plays an important role in determining tumor aggressiveness and chance of post-surgical recurrence.

In some embodiments, additional textural information can be utilized to augment the classifier algorithms described above. In particular, texture feature maps can be computed from other feature maps (e.g., other first-order statistical measures, second order statistical measures, and/or image quality measures). These “texture-of-texture” feature maps can be used additionally or alternatively as an input to a classifier algorithm in order to grade a tumor or provide prognostic information about the likelihood of a tumor's response to a particular treatment regimen.

A general workflow for generating such texture-of-texture maps is illustrated in FIG. 7, where QUS maps are generated from RF data. QUS mean values are computed from those QUS maps and then using a GLCM technique texture feature maps are computed from those QUS mean values. Similarly, a GLCM technique is sued to generate texture feature maps, which are then input to a second pass of the texture analysis (e.g., using a second pass of the GLCM technique) to generate texture-of-texture feature maps.

In the example shown in FIG. 7, for individual QUS features, mean values were determined by averaging QUS values obtained from all the sub-volumes. Four texture features, including contrast (“CON”), energy (“ENE”), correlation (“COR”), and homogeneity (“HOM”), were evaluated using a gray-level co-occurrence matrix (“GLCM”) method to generate QUS-Tex1 features from the QUS parametric maps. Corresponding color-coded QUS-texture feature maps for each QUS parametric map were constructed by making a spatial map of the texture values computed over all the sub-ROI. Subsequently, a second-pass texture analysis using the QUS parametric maps as input was applied to create QUS-Tex1-Tex2 features (texture-of-texture features). A total number of 105 features (5 QUS, 20 QUS-Tex1, 80 QUS-Tex1-Tex2) were determined from ultrasound data for each tumor in this example.

Referring now to FIG. 8, a flowchart is illustrated as setting forth the steps of an example method for using QUS techniques and a texture-of-texture analysis to generate imaging biomarkers that indicate an efficacy of a given treatment on a tumor. Raw echo signal data (e.g., ultrasound RF data) are acquired from the subject, as indicated at step 802. The raw echo signal data are acquired in response to ultrasound transmitted to the subject. The transmitted ultrasound is preferably conventional-frequency, but can also be high-frequency ultrasound. In some instances, it may also be beneficial to use combinations of low-frequency and high-frequency ultrasound depending on the depth of the tissue and the desired imaging resolution. For instance, higher ultrasound frequencies are capable of increasing imaging resolution, but at the cost of limiting the penetration depth of the ultrasound. For example, ultrasound frequencies in the range of 20-60 MHz can achieve imaging resolutions in the range of 30-80 μm, whereas ultrasound frequencies in the range of 1-20 MHz can achieve imaging resolutions of 30 μm to about 1.5 mm.

By way of example, the raw echo signal data can be acquired from a subject using an ultrasound system operating at a conventional ultrasound frequency, such as 6 MHz or 10 MHz. Alternatively, the ultrasound system can be operated to generate high frequency ultrasound, such as greater than 20 MHz. The echo signals in the ultrasound RF data can be obtained in a number of differently oriented image planes.

Images of the subject that depict the subject's anatomy are also provided, as indicated at step 804. As one example, the images can be B-mode images that are provided by acquiring the images from the subject, or by retrieving previously acquired B-mode images from data storage.

One or more regions-of-interest (“ROIs”) are then identified in the provided images of the subject, as indicated at step 806. In general, one or more ROIs are identified for each tumor depicted in the provided images. In some instances, at least two ROIs can be identified for each tumor: one ROI being associated with the core of the tumor and one ROI being associated with the tumor margin.

The ROIs are then divided into RF blocks, as indicated at step 808. As an example, each selected ROI can be divided into RF blocks using a sliding window approach with or without overlap between adjacent windows. Each window is advantageously sized to be larger than the minimum size required to obtain reliable spectral parameters that are independent of window length. For instance, the window can be sized to be at least as large as ten wavelengths of the transmitted ultrasound.

For each RF block, parametric maps (e.g., QUS parameter maps) are generated, as indicated at step 810. For instance, the parametric maps are images whose pixel values are representative of quantitative ultrasound parameters computed from the raw echo signal data. As described above, examples of such parameters include MBF, SS, SI, SAS, ACE, ASD, and AAC. Quantitative ultrasound parameters such as ASD and AAC can be estimated by fitting a theoretical tissue backscatter model to the measured backscatter signal from the tissue of interest. For example, estimated and theoretical backscatter coefficients can be used to compute ASD and AAC. As one example, a linear regression analysis, such as a least squares fit, can be applied to a normalized power spectrum to extract the MBF, SS, and SI parameters, as described above.

One or more texture feature maps, such as first-order statistical measures, second-order statistical measures, and/or image quality measures are then generated from the parametric maps, as indicated at step 812. The texture feature maps can be generated for each sub-volume, or parametric maps can be averaged over different sub-volumes and the texture feature maps generated based on that average. As described above, when generating second-order statistical measures based on a GLCM, a number of different GLCMs can also be constructed for each parametric map. For example, sixteen symmetric GLCMs can be constructed considering each pixel's neighbors located at the displacement distances, d, of one to four pixels with angular values, θ, of 0-135 degrees with 45 degree increments. The second-order statistical measures, or textural features, can then be extracted from the corresponding GLCMs of each QUS parametric map and consequently averaged to produce the computed second-order statistical measures.

In addition to the texture feature maps, texture derivative maps (which may in some instances be referred to as texture-of-texture maps) are also generated, as indicated at step 814. These texture derivative maps can be generated by performing a two or more passes of the texture analysis to the parametric maps, or one or more additional passes of the texture analysis to the texture feature maps generated in step 812. As one non-limiting example, the texture derivate maps can be generated by a second pass of the texture analysis, whether to the texture maps generated in step 812 or to a different set of texture feature maps generated from the parametric maps. In other instances, further sequential texture analyses can be applied to generate even higher order texture derivative maps, such that underlying information can be extracted at finer levels.

Then, as indicated at step 820 the QUS parameter maps, texture feature maps, and texture derivative maps can be input to a classifier to subtype the tumor and to provide a statistical and/or probabilistic estimate indicating whether the tumor is likely to have resistance to certain treatment regimens prior to exposing the tumor to the particular therapy. As described above, the classifier can implement a discriminant analysis, such as a linear discriminant analysis (“LDA”). As one example, a Fisher's linear discriminant (“FLD”) classifier can be used. In other embodiments, other classifiers, such as a support vector machine (“SVM”) classifier or a k-nearest neighbors (“k-NN”) classifier, can be used. In some embodiments, the tumor can be classified based on a prediction of the tumor's response to a chemotherapy treatment. In some other embodiments, the tumor can be classified based on a prognosis for the subject following treatment with a particular tumor treatment regimen.

An FLD-based classifier is a linear classifier that projects multidimensional data onto a feature space that maximizes the ratio of between-class to within-class variance, and performs well when the data can be separated by a line. To classify a tumor to indicate its response to different treatment regimens using LDA (e.g., an FLD-based classifier), the linear discriminant can be trained and tested using, for example, a leave-one-out approach for the tissue being analyzed. Similar training and testing can be performed using other classifiers, such as SVM and k-NN. Examples of tissues that can be analyzed include, but are not limited to, tissues in the breast, liver, brain, prostate, kidney, bladder, gallbladder, spleen, cervix, blood vessels, muscle, and bone. This training can be performed in real-time or, preferably, can be performed off-line with the results stored in a feature set database that can be provided during processing. Such a feature set database includes combinations of the first-order statistical measures, second-order statistical measures, and image quality measures that maximize, or otherwise provide desired levels of, the specificity and sensitivity of the tumor classification. As discussed above, these feature sets can define imaging biomarkers for the tumor and can indicate the tumor response to different treatment regimens before exposing the subject to chemotherapy.

SVM-based classifiers build a model (e.g., from training data) to have the largest possible gap between the classes, and then predict the class association of test data samples based on which side of the gap they fall on. As one example, a Gaussian radial basis function can be used as the kernel function for an SVM-based classifier. In general, the kernel function defines how data samples will be mapped into the new feature space, called kernel space. SVM model parameters can be optimized using a grid search. In a leave-one-out validation scheme, a k-NN classifier can be used to predict the class association of a test point in the feature space based on the class that forms the majority of the points neighboring the point of interest, and based also on the distance between those points and the point of interest. The SVM and k-NN based classifiers are non-linear classifiers, which are advantageous when the classes cannot be separated by a line and when a large number of features is available.

In addition, the systems and methods described in the present disclosure can be used to characterize tissue. As described above, quantitative ultrasound parameters are calculated from raw echo signal data acquired from a region-of-interest during an ultrasound scan. In some instances, these parameters are calculated after normalizing the echo signal data using reference data so as to mitigate the effects of variations in instruments settings, ultrasound beam diffraction, and attenuation effects. Texture feature map data and texture derivative map data are generated from these parameter maps, and used to monitor and/or detect the process of cellular death and/or damage. Any process leading to cellular death can be detected or monitored including, for example, death by apoptosis, necrosis, oncosis, apoptotic necrosis, and apoptotic oncosis. In general, the process of cellular death can be detected and/or monitored by identifying or detecting one or more dead cells, dying cells, damaged cells, or a combination thereof. When the term “cellular death” is used herein, it includes both a dead cell and a cell undergoing a cellular death process (i.e., a dying cell). Dead or dying cells can be monitored or detected by detecting cellular damage of the cell.

Cellular death can be monitored in vitro or in vivo using the disclosed methods. Although it has been possible to differentiate non-invasively between dying and viable cell populations based on changes in integrated backscatter at high frequency, the limited penetration depth of high frequency ultrasound restricts its use to superficial sites. Moreover, the increase in backscatter can be frequency dependent, much weaker at higher frequencies, and thus non-detectable, compared to frequencies equal to or lesser than 20 MHz. As used herein, low frequency ultrasound refers to ultrasound transmitted at a frequency of 20 MHz or lower. Similarly, “low frequency ultrasound imaging” is meant to refer to ultrasound imaging at frequencies of 20 MHz or lower. Thus, ultrasound can be transmitted, or imaging can be performed, at 20 MHz, or at, about or between, 19 MHz, 18 MHz, 17 MHz, 16 MHz, 15 MHz, 14 MHz, 13 MHz, 12 MHz, 11 MHz, 10 MHz, 9 MHz, 8 MHz, 7 MHz, 6 MHz, 5 MHz, 4 MHz, 3 MHz, 2 MHz, 1 MHz, or lower.

Thus, the systems and methods described in the present disclosure can be implemented for monitoring and/or detecting cellular death within a subject. As one non-limiting example, low frequency ultrasound can be used to acquire data from a selected site within the subject, wherein the selected site has been exposed to a stress capable of causing cellular death at the selected site. As stated above, the use of “cellular death” herein includes the process of cellular death. The process of cellular death can include damage to one or more cell and eventual death of that cell. Thus, as referred to herein, methods of monitoring cellular death can be accomplished by detecting cellular damage. A method of detecting cellular damage can include identifying a damaged, dying, or a dead cell. Also, as used herein, the term “monitor” can be used interchangeably with “detect.” Thus, “monitoring” cellular death can be accomplished by detecting a dead or damaged cell.

In these instances, the classifier can be constructed (e.g., trained) using data that measures cellular damage in tissues and correlates those changes to QUS parameters, texture features, and/or texture derivatives. For example, to assess the effect of changes in cell structure on these parameters, texture features, and texture derivatives, data can be collected from cell and/or tissue cultures exposed to a stress capable of causing cellular damage and/or death. For example, stress capable of causing cellular damage and/or death can include any stress that will result in the initiation of apoptosis or cellular death. Examples of such stresses include, but are not limited to, chemotherapeutic agents, drugs, photodynamic therapy, thermal therapy, cryotherapy, chemical modifiers aimed at protecting tissues from radiations such as sunscreens, radiations including X-rays, gamma rays and ultraviolet radiations, oxygen and/or nutrient deprivation that can occur after organ removal for transplantation for example, and the activation of genes that can initiate an apoptotic response as well as aging and developmental processes. Accordingly, the term “stress capable of causing cellular damage and/or death” is also meant to encompass biological events that occur normally in tissues to induce apoptosis or cellular death. Cellular death or damage caused by necrosis can also be monitored or detected.

The cell and/or tissue cultures are also imaged with ultrasound (e.g., before and after inducing stress to the cell and/or tissue cultures) and parameter maps, texture feature maps, and/or texture derivative maps are computed. The classifier(s) can then be constructed using these inputs as training data.

As described above, QUS parameter map data, texture feature map data, and/or texture derivative map data can then be input to a classifier to characterize tissues. In some instances, characterizing the tissue can include measuring, monitoring, or otherwise detecting cellular damage in the tissue. For instance, inputting the data to the classifier can generate output that indicates a prediction of cellular damage in a tissue, a classification of the degree of cellular damage in the tissue, and/or a classification of whether cells in the tissue are dying or already dead.

Example #2: Classifying Tumors in Locally Advanced Breast Cancer

In an example study, the use of QUS radiomics to predict neoadjuvant chemotherapy (“NAC”) response in patients with locally advanced breast cancer (“LABC”) was investigated. This study incorporated higher-order imaging features in the form of “texture derivatives,” as described above.

100 patients with LABC were scanned with 6 MHz ultrasound before starting NAC. Five QUS parametric image-types were generated from radio-frequency data over the tumor volume. From each QUS parametric map, four GLCM-based texture feature maps were derived (20 QUS-Tex1), which were further processed to create texture derivatives (80 QUS-Tex1-Tex2), or “texture-of-texture” feature maps. Patients were classified into two groups based on clinical/pathological responses to treatment: responders and non-responders. Three machine learning algorithms based on FLD, KNN, and SVM were used for developing radiomic models of response prediction.

Representative ultrasound B-mode images, MBF parametric images, and MBF-CON, MBF-ENE, MBF-COR, and MBF-HOM texture images corresponding to three responding and three non-responding patients are shown in FIG. 9. None of the parameters demonstrated significant differences in terms of distribution between the two groups. The classification performances using the different algorithms have been summarized below in Table 5. The accuracies from QUS and QUS-Tex1 features using FLD, KNN, and SVM classifiers were 62%, 69%, and 69%, respectively. Selecting from 105 features inclusive of the texture derivatives, accuracy improved to 67%, 82%, and 69% for FLD, KNN, and SVM, respectively. Amongst all the models used in this example study, KNN classification resulted in best performances. The sensitivity, specificity, accuracy, and AUC for the best classifier was 87%, 81%, 82%, and 0.86, respectively.

TABLE 5 Classifier Features Sensitivity Specificity Accuracy AUC Selected parameters FLD QUS-Tex¹ 73% 60% 62% 0.60 SI-COR QUS-Tex¹-Tex² 67% 67% 67% 0.61 SI-COR KNN QUS-Tex¹ 67% 69% 69% 0.74 SI-COR QUS-Tex¹-Tex² 87% 81% 82% 0.86 AAC-CON-ENE, MBF- COR-ENE, SI-COR-ENE SVM QUS-Tex¹ 74% 67% 69% 0.79 MBF, AAC-ENE, ASD-ENE QUS-Tex¹-Tex² 74% 67% 69% 0.79 SI-ENE, ASD-ENE-CON, MBF-COR-COR

The best-selected features for model development based on this example study are summarized in Table 5. The model used one feature for classification when no further improvement (or decrease) in the classifier performance was evident on the subsequent addition of second or third features (for FLD both models and KNN-texture model). The best classifier model (KNN-texture derivatives) used three higher-order features (AAC-CON-ENE), (MBF-COR-ENE, SI-COR-ENE) for the group classification.

The median follow up for all patients was 54 months (range 6 to 105 months). At last follow up, recurrence was seen in 20 patients. Among the responders, 15% of patients had failed, while 45% of patients had failed among the non-responders. Five-year recurrence-free survival (“RFS”) for responders and non-responders was 94% and 74%, respectively (p=0.002). For the purposes of studying the impact of predicted responses from the radiomics model on recurrence, the RFS was calculated differentially for the three models using texture derivatives. The best values were obtained from the KNN model, which resembled the actual RFS observed using clinical/pathological response. The KNN-predicted responders demonstrated a 5-year RFS of 94% as compared to 76% for KNN-predicted non-responders (p=0.006).

The clinical application of QUS spectral and texture analysis can be used to identify a subset of patients likely to respond to NAC as early as four weeks (serial scans) or even before the initiation of treatment (baseline imaging). In the example study described here, five spectral features were included, and a set of 20 texture (QUS-Tex1) and 80 higher-order texture derivatives (QUS-Tex1-Tex2) were generated. Each spectral parameter reflects different aspects of underlying cellular biology. For instance, MBF, ASD, and AAC can be related to microstructure and cellular organization, scatterer size and scatterer number density.

As noted above, in the example study, the KNN model exhibited the best diagnostic matrices, with all the three features selected by the algorithm being texture-derivatives. This resulted in better classifier performance as evident from improvements in AUC. It is possible that the second-order texture derivatives better represent intratumoral heterogeneity, thereby leading to better prediction of clinical outcomes. In addition, it may be possible with further sequential texture analysis that underlying information is extracted at finer levels, which otherwise is obscured.

The early identification of patients not responding to NAC has advantageous clinical implications. For instance, alternative chemotherapy regimens or upfront surgery can be considered in patients who are refractory to the standard protocols, rather than continuing with the ineffective regimens. The systems and methods described in the present disclosure enable identifying such patients ahead of time, helping in planning their treatment.

The present invention has been described in terms of one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention. 

1. A computer-implemented method for assessing a prediction of tumor response to a treatment using an ultrasound system, the steps of the method comprising: (a) acquiring ultrasound echo signal data from a subject using the ultrasound system, wherein the ultrasound echo signal data are acquired from a volume containing a tumor; (b) generating parametric map data from the ultrasound echo signal data, wherein the parametric map data have pixel values associated with one or more different quantitative parameters computed from the acquired ultrasound echo signal data; (c) generating texture derivative map data by inputting the parametric map data to two or more passes of a texture analysis algorithm, generating output as the texture derivative map data, wherein the texture derivative map data have pixel values associated with one or more different texture derivatives; and (d) classifying the tumor by applying a classifier to the texture derivative map data, wherein classifying the tumor indicates a prediction of the tumor's response to a treatment.
 2. The method as recited in claim 1, wherein classifying the tumor includes applying the classifier to both the parametric map data and the texture derivative map data.
 3. The method as recited in claim 1, further comprising: generating texture feature map data by inputting the parametric map data to a texture analysis algorithm, generating output as the texture feature map data, wherein the texture feature map data have pixel values associated with one or more different texture features; and wherein classifying the tumor includes applying the classifier to both the texture map data and the texture derivative map data.
 4. The method as recited in claim 3, wherein classifying the tumor includes applying the classifier to the parametric map data, the texture map data, and the texture derivative map data.
 5. The method as recited in claim 3, wherein the texture derivative map data are generated by inputting the parametric map data to a first pass of the texture analysis algorithm, generating output as the texture map data, and then inputting the texture map data to a second pass of the texture analysis algorithm, generating output as the texture derivative map data.
 6. The method as recited in claim 1, wherein each of the one or more texture derivatives represent a sequential computing of a texture feature.
 7. The method as recited in claim 6, wherein the texture feature is at least one of contrast, energy, homogeneity, correlation, autocorrelation, dissimilarity, gray-level co-occurrence matrix variability, entropy, cluster shade, cluster prominence, and maximum probability.
 8. The method as recited in claim 6, wherein the texture feature is at least one of a mean, a standard deviation, a skewness, and a kurtosis.
 9. The method as recited in claim 1, wherein each of the one or more different quantitative parameters are selected from the group consisting essential of mid-band fit, spectral slope, spectral 0-MHz intercept, attenuation coefficient estimate, average scatterer diameter, and average acoustic concentration.
 10. The method as recited in claim 1, wherein step (b) includes computing a normalized power spectrum of the ultrasound echo signal data and generating the parametric map data by computing the one or more different quantitative parameters from the normalized power spectrum.
 11. The method as recited in claim 10, wherein step (b) includes providing reference echo signal data obtained from a phantom using the ultrasound system and computing the normalized power spectrum using the provided reference echo signal data such that effects from the ultrasound system are minimized in the normalized power spectrum.
 12. The method as recited in claim 1, wherein the classifier is at least one of a Fisher's linear discriminant classifier, a support vector machine classifier, or a k-nearest neighbors classifier.
 13. A computer-implemented method for using an ultrasound system to assess a prognosis for a subject who will be treated with a particular tumor treatment regimen, the steps of the method comprising: (a) acquiring ultrasound echo signal data from a subject using the ultrasound system, wherein the ultrasound echo signal data are acquired from a volume containing a tumor; (b) generating parametric map data from the ultrasound echo signal data, wherein the parametric map data have pixel values associated with one or more different quantitative parameters computed from the acquired ultrasound echo signal data; (c) generating texture derivative map data by inputting the parametric map data to two or more passes of a texture analysis algorithm, generating output as the texture derivative map data, wherein the texture derivative map data have pixel values associated with one or more different texture derivatives; and (d) classifying the tumor by applying a classifier to the texture derivative map data, wherein classifying the tumor indicates a prognosis for the subject following treatment with a particular tumor treatment regimen.
 14. The method as recited in claim 13, wherein the prognosis for the subject is based on an estimator selected from the group consisting of a progression-free survival, a survival rate, and a survival time.
 15. The method as recited in claim 13, wherein classifying the tumor includes applying the classifier to both the parametric map data and the texture derivative map data.
 16. The method as recited in claim 13, further comprising: generating texture feature map data by inputting the parametric map data to a texture analysis algorithm, generating output as the texture feature map data, wherein the texture feature map data have pixel values associated with one or more different texture features; and wherein classifying the tumor includes applying the classifier to both the texture map data and the texture derivative map data.
 17. The method as recited in claim 16, wherein classifying the tumor includes applying the classifier to the parametric map data, the texture map data, and the texture derivative map data.
 18. The method as recited in claim 16, wherein the texture derivative map data are generated by inputting the parametric map data to a first pass of the texture analysis algorithm, generating output as the texture map data, and then inputting the texture map data to a second pass of the texture analysis algorithm, generating output as the texture derivative map data.
 19. The method as recited in claim 13, wherein each of the one or more texture derivatives represent a sequential computing of a texture feature.
 20. The method as recited in claim 19, wherein the texture feature is at least one of contrast, energy, homogeneity, correlation, autocorrelation, dissimilarity, gray-level co-occurrence matrix variability, entropy, cluster shade, cluster prominence, and maximum probability.
 21. The method as recited in claim 19, wherein the texture feature is at least one of a mean, a standard deviation, a skewness, and a kurtosis.
 22. The method as recited in claim 13, wherein each of the one or more different quantitative parameters are selected from the group consisting essential of mid-band fit, spectral slope, spectral 0-MHz intercept, attenuation coefficient estimate, average scatterer diameter, and average acoustic concentration.
 23. The method as recited in claim 13, wherein the classifier is at least one of a Fisher's linear discriminant classifier, a support vector machine classifier, or a k-nearest neighbors classifier.
 24. A computer-implemented method for detecting cellular damage in tissue using an ultrasound system, the steps of the method comprising: (a) acquiring ultrasound echo signal data from a subject using the ultrasound system, wherein the ultrasound echo signal data are acquired from a volume containing a tissue; (b) generating parametric map data from the ultrasound echo signal data, wherein the parametric map data have pixel values associated with one or more different quantitative parameters computed from the acquired ultrasound echo signal data; (c) generating texture derivative map data by inputting the parametric map data to two or more passes of a texture analysis algorithm, generating output as the texture derivative map data, wherein the texture derivative map data have pixel values associated with one or more different texture derivatives; and (d) detecting cellular damage in a tissue in the subject by applying a classifier to the texture derivative map data, wherein detecting the cellular damage comprises classifying the tissue as corresponding to a prediction of cellular damage.
 25. The method as recited in claim 24, wherein the ultrasound echo signal data are acquired using ultrasound transmitted with a frequency greater than 0 MHz and less than 20 Mhz.
 26. The method as recited in claim 24, wherein detecting cellular damage includes applying the classifier to both the parametric map data and the texture derivative map data. 